首页 | 本学科首页   官方微博 | 高级检索  
     

支持在线学习的增量式极端随机森林分类器
引用本文:王爱平,万国伟,程志全,李思昆.支持在线学习的增量式极端随机森林分类器[J].软件学报,2011,22(9):2059-2074.
作者姓名:王爱平  万国伟  程志全  李思昆
作者单位:国防科学技术大学 计算机学院,湖南 长沙,410073
基金项目:国家自然科学基金(90707003,60970094)
摘    要:提出了一种增量式极端随机森林分类器(incremental extremely random forest,简称IERF),用于处理数据流,特别是小样本数据流的在线学习问题.IERF算法中新到达的样本将被存储到相应的叶节点,并通过Gini系数来确定是否对当前叶节点进行分裂扩展,在给定有限数量,甚至是少量样本的情况下,I...

关 键 词:在线学习  增量学习  极端随机森林分类器
收稿时间:2009/8/22 0:00:00
修稿时间:2009/10/23 0:00:00

Incremental Learning Extremely Random Forest Classifier for Online Learning*
WANG Ai-Ping,WAN Guo-Wei,CHENG Zhi-Quan and LI Si-Kun.Incremental Learning Extremely Random Forest Classifier for Online Learning*[J].Journal of Software,2011,22(9):2059-2074.
Authors:WANG Ai-Ping  WAN Guo-Wei  CHENG Zhi-Quan and LI Si-Kun
Affiliation:WANG Ai-Ping,WAN Guo-Wei,CHENG Zhi-Quan,LI Si-Kun(College of Computer,National University of Defense Technology,Changsha 410073,China)
Abstract:This paper proposes an incremental extremely random forest(IERF) algorithm,dealing with online learning classification with streaming data,especially with small streaming data.In this method,newly arrived examples are stored at the leaf nodes and used to determine when to split the leaf nodes combined with Gini index,so the trees can be expanded efficiently and fast with a few examples.The proposed online IERF algorithm gives more competitive or even better performance,than the offline extremely random fore...
Keywords:online learning  incremental learning  extremely random forest classifier  
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《软件学报》浏览原始摘要信息
点击此处可从《软件学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号